High-quality Activity-Level Video Advertising

Online video advertising is a billion dollar business, but the current low CTR reveals the huge potential for improvement in the ad serving quality. In this work, we present a novel activity-level video advertising system named ActVa. Different from existing systems that assume a fixed scope of ad keywords, ActVA enables advertising targeted to non-predefined activities in a highly efficient way requiring no training data for diverse activities. To achieve this goal, a general and extensible graphical representation of both video content and advertising demand is proposed to embed multimodal content at the activity level. Our ad-content relevancy measurement can achieve 10,000 FPS retrieval speed. We model the ads assigning task as an optimization problem taking content relevance, ads revenue as well as viewer experience into consideration. A non-maximal suppression based algorithm is designed to significantly reduce the algorithm complexity for online ad serving. Our extensive objective and subjective experimental results show the effectiveness and efficiency of ActVA. ActVA can effectively uncovers numerous high-quality (content-relevant) advertising opportunities and delivers ads to viewers in a profitable and user-friendly way.